name stringlengths 18 61 | instruction stringlengths 4.62k 7.32k | description stringclasses 1
value | keywords listlengths 0 0 | schema_version stringclasses 1
value | config unknown | metadata unknown | files listlengths 69 178 | location stringlengths 24 34 |
|---|---|---|---|---|---|---|---|---|
Capacity-resolved knapsack audit | # Capacity-resolved knapsack audit
Implement `/app/solve.py`. It must compute the integer optimum, fractional optimum, and normalized integrality gap for every integer capacity of each supplied instance. All weights, profits and capacities are dimensionless. A positive gap can occur even when the integer solution is o... | [] | {
"task": {
"id": "capacity_knapsack_audit",
"name": "Capacity-resolved knapsack audit",
"version": "1.0"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "1e802518efa11ddaa824da07d6341c5534fa276caae4aaa00f3b32b868a77c55",... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/audit_feedback.py",
"environment/public/inputs/instances.json",
"environment/public/inputs/public.json",
"environment/public/public_reference.json",
"environment/public/solve.py",
"environment/public/validate.py",
... | tasks/cs-ds-2512-21195v2 | ||
Sparse Markov prefix codec | # Sparse context-dependent prefix codec
Implement `/app/codec.py`, a CPU-only codec for bounded directed graph-walk corpora. No external data are needed. Use the Python CLI
```
python /app/codec.py --input REQUEST.json --output RESULT.json
```
The input's `mode` is either `fit-encode` or `decode`. Create any missing... | [] | {
"task": {
"id": "arxiv_2512_21320v2_idea0",
"name": "Sparse Markov prefix codec",
"version": "1.0"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "f531e7323cf12e871db9023364caaf0745b112a95e51e08cc9f43b108cb70806",
... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/feedback_core.py",
"environment/public/feedback_example.py",
"environment/public/inputs/public/answers.json",
"environment/public/inputs/public/decode.json",
"environment/public/inputs/public/fit.json",
"environment... | tasks/cs-ds-2512-21320v2 | ||
Certified minimax marginal noise | # Certified minimax Gaussian covariances for marginal tables
Implement `/app/solve.py`. Run it as `python /app/solve.py --inputs INPUT.json --outputs OUTPUT.json`. Both arguments are required. Create a missing output parent. Inputs normally live under `/app/inputs` and outputs under `/app/outputs`.
## Workloads and o... | [] | {
"task": {
"id": "gaussian_marginal_certificates",
"name": "Certified minimax marginal noise"
},
"verifier": {
"timeout_sec": 200000
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "6455fa001a6edd9e0efb226e060fc24c3e9c96009aca94a5f6198f39edfc5fb4",
"execut... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/certificate.py",
"environment/public/inputs/calibration.json",
"environment/public/inputs/public.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environment/runt... | tasks/cs-ds-2512-21499v1 | ||
Global directed-energy audit | # Global directed-energy audit
Implement `/app/outputs/audit.py`. This is a CPU-only scientific file task. All quantities are dimensionless.
## Input and interface
Run `python /app/outputs/audit.py --input INPUT --output OUTPUT`. With no arguments use `/app/inputs/pairs.json` and `/app/outputs/results.json`. Create m... | [] | {
"task": {
"id": "directed_energy_audit",
"name": "Global directed-energy audit"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "59663358b941b76feff89e244ac934bb921eb8fee8e7699557036e1ad11a5ef0",
"execution_disclosure"... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/check_output.py",
"environment/public/inputs/pairs.json",
"environment/public/inputs/public.json",
"environment/public/outputs/audit.py",
"environment/public/public_margins.json",
"environment/public/science.py",
... | tasks/cs-ds-2512-21671v2 | ||
Compile shared-frontier binary tiles | # Compile shared-frontier binary tiles
Implement a CPU-executable compiler for 128 independent eight-bit masked population counts. This is an algebraic lowering problem, not a GPU implementation or performance benchmark. No graph data or accelerator is needed.
## Interface
Create `/app/solution.py`. It must run as:
... | [] | {
"task": {
"id": "shared_frontier_tiles",
"name": "Compile shared-frontier binary tiles",
"version": "1.0"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "22000061c949bb79ad637951ac7a566136ddf5532e19711198acffff0f63bff1... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/cert_io.py",
"environment/public/evaluate.py",
"environment/public/inputs/masks.json",
"environment/public/prepare_public.py",
"environment/public/solution.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.... | tasks/cs-ds-2512-21967v1 | ||
Exact signed bilinear circuit compilation | # Compile exact signed bilinear circuits
Implement `/app/compiler.py`. Invocation:
```
python /app/compiler.py --input INPUT_JSON --output OUTPUT_JSON
```
Create the output parent directory if it does not exist. Explicit arguments are required; there are no no-argument defaults. A public example is `/app/inputs/publ... | [] | {
"task": {
"id": "arxiv_2512_21980v1_idea0",
"name": "Exact signed bilinear circuit compilation"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "932450d42901787396816d185be98b746eee4aac2a9e292c34d4eb2c7b6375b9",
"execut... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/public.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environment/runtime/wheels/numpy-2.2.6-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
"... | tasks/cs-ds-2512-21980v1 | ||
cs-ds-2512-22475v1 | # Maximum-weight connected colorful subsets
Implement `/app/solve.py` as a Python command-line program. For each colored, weighted, simple undirected graph, find a maximum-weight connected set containing exactly one vertex of every color, or correctly report infeasibility.
## Mathematical target
Vertices are labeled... | [] | {
"task": {
"id": "connected_colorful_subset",
"title": "Exact connected colorful subset optimization"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "26c10357378a5662ad284fea7bf8dd56cb2059290f529f2cf6c3e134fc311fbc",
"e... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/public.json",
"environment/public/public_values.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environment/runtime/wheels/numpy-2.2.6-cp311-cp311-manylinux... | tasks/cs-ds-2512-22475v1 | ||
cs-ds-2512-22803v2 | # Finite-size rank-one spin covariance diagnostic
Implement `/app/solve.py`. This diagnostic compares an exact finite spin covariance with a saddle-point heuristic; no equality or asymptotic error bound for the heuristic is assumed.
## Model and targets
All quantities are dimensionless. A case has integer `n` with 2 ... | [] | {
"task": {
"id": "arxiv_2512_22803v2_idea0",
"title": "Finite-size rank-one covariance diagnostic"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "ded1c031be86e323cde871e6dd656206e642cb4d16ea9e55971c3325d77edc39",
"exec... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/check_public.py",
"environment/public/inputs/cases.json",
"environment/public/inputs/public.json",
"environment/public/public_reference.json",
"environment/public/science_core.py",
"environment/public/validate.py",
... | tasks/cs-ds-2512-22803v2 | ||
cs-ds-2512-22975v1 | # Minimum interval-modular partitions
Implement `/app/solve.py`, a CPU-only command-line program that exactly minimizes the number of interval-module blocks of each input graph. No asymptotic running-time claim is graded.
## Mathematics
A graph is simple, undirected, unweighted and dimensionless. Its vertices are th... | [] | {
"task": {
"id": "interval_modular_partitions",
"title": "Minimum interval-modular partitions"
},
"verifier": {
"timeout_sec": 2400
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "2c02fd2d44d0582c3dd968ff3d7741bf4cee8532bb7044b87804925fa92d95b3",
"executi... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/format_io.py",
"environment/public/inputs/graphs.json",
"environment/public/inputs/public.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environment/runtime/whe... | tasks/cs-ds-2512-22975v1 | ||
Exact permutation compatibility rectangles | # Exact rectangles in permutation compatibility restrictions
Implement `/app/solve.py`, a CPU-only exact solver for the following finite combinatorial optimization problem. All quantities are dimensionless integers. No floating-point approximation or tolerance is used.
## Mathematical target
A permutation of order `... | [] | {
"task": {
"id": "permutation_rectangles",
"name": "Exact permutation compatibility rectangles"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "e6b87183a09760e70d73fa3d787766101adc27a85cbcbeb74c09e419840b6dda",
"execut... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/cases.json",
"environment/public/inputs/public.json",
"environment/public/public_reference.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environment/run... | tasks/cs-ds-2512-23121v1 | ||
Exact active-weight visibility | # Exact active-weight visibility
Implement `/app/evaluate.py`, a CPU-only exact evaluator of a supplied visibility-weight state. This is an integration task, not a search for guard positions.
## Geometry and target
An outer counterclockwise simple polygonal ring and zero, one, or two clockwise simple hole rings defi... | [] | {
"task": {
"id": "exact_active_visibility",
"name": "Exact active-weight visibility",
"version": "1.0"
},
"verifier": {
"timeout_sec": 3600
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "0e3b1451bd8cf0d636840da032935f6170928c35bfbe35b16e5b56f2a845020a",
... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/evaluate.py",
"environment/public/inputs/cases.json",
"environment/public/inputs/public.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environment/runtime/wheel... | tasks/cs-ds-2512-23297v2 | ||
cs-ds-2512-23468v1 | # Exact minimum-cut isolation profiles
Implement `/app/solve.py` using ordinary CPU computation. The task is to recover complete tied cut families, not just one minimum cut. No efficiency claim beyond the execution budget is required.
## Input and command
Run `python /app/solve.py --input INPUT --output OUTPUT`. Bot... | [] | {
"task": {
"id": "arxiv_2512_23468v1_idea0",
"title": "Exact cut isolation profiles"
},
"verifier": {
"timeout_sec": 1200
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "6f99fc8ccdbe3375a442a9ad8ee8c5af0852334012735679891675a9b672161b",
"execution_disclos... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/public.json",
"environment/public/profile_format.py",
"environment/public/public_table.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environment/runtime... | tasks/cs-ds-2512-23468v1 | ||
Exact finite sparse-projection distortion laws | # Exact finite sparse-projection distortion laws
Implement `/app/solution.py`, a command-line program computing exact finite probability distributions. Use only CPU computation; all algorithms and standard libraries are permitted.
## Scientific target
Each case has dimensionless integers `k`, `s`, and a coefficient v... | [] | {
"task": {
"id": "finite_sparse_projection_histograms",
"name": "Exact finite sparse-projection distortion laws"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "9fc3f36d78d76ae4a7cf0e4c6903d72c87691ab84c8d91a80c43e29acf453... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/format_rules.py",
"environment/public/inputs/cases.json",
"environment/public/inputs/public_cases.json",
"environment/public/public_table.json",
"environment/public/validate.py",
"environment/runtime/wheels/mpmath-1... | tasks/cs-ds-2512-23756v1 | ||
Stochastic diffusion optimal control | # Stochastic diffusion optimal control
Implement `/app/solve.py`. Solve the finite-dimensional, dimensionless optimization problem below. Any mathematically equivalent numerical algorithm is allowed; there is no speed score.
## Input and CLI
Run `python /app/solve.py --input INPUT.json --output OUTPUT.json`. Both ar... | [] | {
"task_id": "stochastic_diffusion_control",
"version": "1.0",
"task": {
"name": "Stochastic diffusion optimal control",
"category": "scientific-computing"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "cf0d3efe4c654e6... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/feedback_core.py",
"environment/public/feedback_data/analytic.json",
"environment/public/feedback_data/coupled.json",
"environment/public/feedback_one.py",
"environment/public/inputs/public/analytic.json",
"environm... | tasks/math-na-2512-23804v2 | ||
Conforming space-time wave projection | # Space-time wave least-squares projection
Implement `/app/solver.py`. This is a finite-dimensional approximation task, not a claim about approximation of all weak wave solutions. Use any numerical method that satisfies the acceptance rule.
## Mathematical target
All quantities are dimensionless. On (0,1), the orthon... | [] | {
"task": {
"id": "wave_graph_projection",
"name": "Conforming space-time wave projection"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "55c94125a30b938a0be9b5b52f2f1df063198bf007ef49965e0995a3008a4fff",
"execution_di... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/cases.json",
"environment/public/inputs/public.json",
"environment/public/solver.py",
"environment/public/validation/check.py",
"environment/public/validation/metrics.py",
"environment/public/validation/publi... | tasks/math-na-2512-23807v2 | ||
math-na-2512-23814v1 | # Complex-affine stability lower envelopes
Implement `/app/outputs/scm.py`, a CPU Python program evaluating a constrained Rayleigh-coordinate relaxation. NumPy and SciPy are available. All quantities are dimensionless.
## Mathematics
For three complex matrices, let `M[0] = i I` and
\(M(t)=\sum_{j=1}^3 t_j M_j\), with... | [] | {
"task": {
"id": "arxiv_2512_23814v1_idea0",
"title": "Complex-affine stability lower envelopes"
},
"verifier": {
"timeout_sec": 900
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "f13905cc758024e9067c12e8336f94068115c218a9edc19bcbfe3a83baa7ce4c",
"execut... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/check_scm.py",
"environment/public/inputs/public_optima.json",
"environment/public/inputs/scm.json",
"environment/public/outputs/scm.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl",
"environme... | tasks/math-na-2512-23814v1 | ||
Certified max-affine quadratic prior recovery | # Recover a prior from a max-affine quadratic potential
Implement `/app/solve.py`. All quantities are dimensionless. For each query y, compute the conjugate and the recovered prior of
psi(z) = c ||z||_2^2 / 2 + max_i (b_i dot z + r_i),
C(y) = sup_z [y dot z - psi(z)],
J(y) = [C(y) - ||y||_2^2 / 2] / t.
The rows b_i ... | [] | {
"task": {
"id": "arxiv_2512_23829v3_idea0",
"name": "Certified max-affine quadratic prior recovery"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "41467cff7ba2a094086acd6c9879656bec00bbd3eff0f77c594fe7c09fe030be",
"e... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/certificate.py",
"environment/check.py",
"environment/public/certificate.py",
"environment/public/inputs/constants.json",
"environment/public/inputs/public.npz",
"environment/public/solve.py",
"environment/public/validat... | tasks/math-na-2512-23829v3 | ||
Planar reflected cell occupation moments | # Planar reflected nearest-site cell integrals
Implement `/app/outputs/solve.py` in Python. It must support
`python /app/outputs/solve.py --input INPUT --output OUTPUT` and, with no arguments, read `/app/inputs/cases.json` and write `/app/outputs/results.json`. Create missing output parent directories. No external dat... | [] | {
"task": {
"id": "planar_reflected_cells",
"name": "Planar reflected cell occupation moments"
},
"verifier": {
"timeout_sec": 600
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "4d43a88015416170ee2784cf1ee92265ea974b707aab71a260af65dd0d0a9117",
"execution... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/cases.json",
"environment/public/check_output.py",
"environment/public/check_public.py",
"environment/public/expected.json",
"environment/public/metrics.py",
"environment/runtime/wheels/mpmath-1.3.0-py3-none-any.whl... | tasks/math-na-2512-23843v1 | ||
Temperature-aware Gaussian-mixture drift and spatial Jacobian | # Temperature-aware Gaussian-mixture drift
Create `/app/outputs/solver.py`, a CPU Python program that evaluates a drift and its spatial Jacobian. All quantities are dimensionless. Implement the mathematical target, not a target-density score approximation.
## Mathematical target
For dimension d, positive mixture weig... | [] | {
"task_id": "temperature_mixture_drift",
"task": {
"name": "Temperature-aware Gaussian-mixture drift and spatial Jacobian"
},
"verifier": {
"timeout_sec": 1800
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "908610662d621e5e5797055b0e5bbe22de1491b0c0bdb2bd184d0... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/inputs/public.npz",
"environment/public/inputs/public_arrays.json",
"environment/public/outputs/solver.py",
"environment/public/validation/check.py",
"environment/public/validation/compare.py",
"environment/public/v... | tasks/math-na-2512-23965v2 | ||
Periodic quadratic-control Bellman trajectories | # Periodic quadratic-control Bellman trajectories
Implement `/app/solver.py` for a dimensionless two-dimensional, fully discrete control problem on the unit torus. Only values are requested: different optimal controls, including ties between periodic lifts, are equivalent.
## Inputs and mathematical target
An NPZ fi... | [] | {
"task": {
"id": "periodic_quadratic_bellman",
"name": "Periodic quadratic-control Bellman trajectories"
},
"verifier": {
"timeout_sec": 2400
},
"execution": {
"schema": "scientific_execution/v1",
"instruction_sha256": "013ed4856baa402c6302b9b3b6f378b97b1901bb84b4cc397584dcabb8c87bad",
... | {} | [
"README.md",
"environment/Dockerfile",
"environment/RUNTIME.md",
"environment/public/acceptance.py",
"environment/public/feedback.py",
"environment/public/inputs/case.npz",
"environment/public/inputs/constant.json",
"environment/public/inputs/interior.json",
"environment/public/inputs/public/constan... | tasks/math-na-2512-24051v2 |
End of preview. Expand in Data Studio
Campaign64 scientific computing tasks
49 containerized scientific-computing tasks generated by an automated paper-to-task synthesis pipeline: cs-ds (13), math-na (12), physics-comp-ph (10), stat-co (14). Each task asks an agent to implement a numerical program; a private file grader runs it on hidden cases and checks scientific predicates and tolerances.
Status: development tasks, not a calibrated benchmark release. Each task passed its own clean Docker verification (reference solution passes, starter and every quality probe fail, positive controls pass). No independent validator, review or acceptance has checked them; the pipeline's own release gate is not passed. Treat difficulty numbers below as unreviewed measurements.
One task of the 50 evaluated, physics-comp-ph-2512-23433v4, is withheld: its grader requests a .json
output path for NPZ content, which the current framework cannot yet declare (see manifest.json).
Hidden tests are included. tests/ (grading cases, references, checker) and solution/ are
published here, so these tasks are not a held-out benchmark.
Layout
tasks/<task>/
instruction.md agent-facing task statement
task.toml task metadata (timeouts, resources)
environment/ Dockerfile, public files, offline wheels
tests/ private grader (test.sh -> file_grader.py), fixtures, references
solution/ reference solution (solve.sh)
positive_controls/ alternative correct solutions (must pass)
quality_probes/ plausible wrong solutions (must fail)
manifest.json per-task digests, sha256 of every file, Luna results
Luna results (unreviewed)
gpt-5.6-luna (high reasoning) in the Terminus 2 harness, 3 attempts per task, 24-turn cap, 1 CPU / 2 GiB, no network: 131/147 attempts passed. Nine tasks were re-evaluated after a grader output-path defect was fixed; the counts below use the re-evaluation for those nine.
| Task | Field | Passed |
|---|---|---|
| cs-ds-2512-21195v2 | cs-ds | 3/3 |
| cs-ds-2512-21320v2 | cs-ds | 3/3 |
| cs-ds-2512-21499v1 | cs-ds | 3/3 |
| cs-ds-2512-21671v2 | cs-ds | 3/3 |
| cs-ds-2512-21967v1 | cs-ds | 3/3 |
| cs-ds-2512-21980v1 | cs-ds | 0/3 |
| cs-ds-2512-22475v1 | cs-ds | 3/3 |
| cs-ds-2512-22803v2 | cs-ds | 3/3 |
| cs-ds-2512-22975v1 | cs-ds | 3/3 |
| cs-ds-2512-23121v1 | cs-ds | 3/3 |
| cs-ds-2512-23297v2 | cs-ds | 2/3 |
| cs-ds-2512-23468v1 | cs-ds | 3/3 |
| cs-ds-2512-23756v1 | cs-ds | 3/3 |
| math-na-2512-23804v2 | math-na | 3/3 |
| math-na-2512-23807v2 | math-na | 3/3 |
| math-na-2512-23814v1 | math-na | 2/3 |
| math-na-2512-23829v3 | math-na | 3/3 |
| math-na-2512-23843v1 | math-na | 3/3 |
| math-na-2512-23965v2 | math-na | 3/3 |
| math-na-2512-24051v2 | math-na | 3/3 |
| math-na-2512-24127v1 | math-na | 3/3 |
| math-na-2512-24401v1 | math-na | 0/3 |
| math-na-2512-24405v3 | math-na | 2/3 |
| math-na-2512-24634v1 | math-na | 3/3 |
| math-na-2601-00038v2 | math-na | 3/3 |
| physics-comp-ph-2512-22670v2 | physics-comp-ph | 3/3 |
| physics-comp-ph-2512-22717v1 | physics-comp-ph | 3/3 |
| physics-comp-ph-2512-22994v1 | physics-comp-ph | 2/3 |
| physics-comp-ph-2512-23056v1 | physics-comp-ph | 3/3 |
| physics-comp-ph-2512-23057v1 | physics-comp-ph | 2/3 |
| physics-comp-ph-2512-23061v1 | physics-comp-ph | 3/3 |
| physics-comp-ph-2512-23117v2 | physics-comp-ph | 3/3 |
| physics-comp-ph-2512-23315v1 | physics-comp-ph | 3/3 |
| physics-comp-ph-2512-23358v2 | physics-comp-ph | 3/3 |
| physics-comp-ph-2512-23396v2 | physics-comp-ph | 3/3 |
| stat-co-2512-17868v2 | stat-co | 2/3 |
| stat-co-2512-18139v1 | stat-co | 3/3 |
| stat-co-2512-18166v1 | stat-co | 2/3 |
| stat-co-2512-18255v1 | stat-co | 3/3 |
| stat-co-2512-18858v1 | stat-co | 2/3 |
| stat-co-2512-18884v2 | stat-co | 2/3 |
| stat-co-2512-19398v2 | stat-co | 3/3 |
| stat-co-2512-19589v1 | stat-co | 3/3 |
| stat-co-2512-19843v1 | stat-co | 3/3 |
| stat-co-2512-20051v1 | stat-co | 2/3 |
| stat-co-2512-20385v1 | stat-co | 3/3 |
| stat-co-2512-21478v1 | stat-co | 3/3 |
| stat-co-2512-21700v1 | stat-co | 3/3 |
| stat-co-2601-00015v1 | stat-co | 3/3 |
Licensing
Task text, graders and fixtures were generated by the pipeline. Bundled wheels under
environment/runtime/wheels/ keep their upstream licenses. Maintainer provenance is in tests/PROVENANCE.md.
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